CDO Data Quality Management 1 — Questions and Answers
Question 1: What is the primary purpose of a Data Quality Management (DQM) framework within an organization?
- To reduce IT infrastructure costs
- To ensure data is accurate, complete, consistent, and fit for use (Correct answer)
- To accelerate software development cycles
- To enforce cybersecurity protocols
Correct answer: To ensure data is accurate, complete, consistent, and fit for use
A DQM framework ensures data meets quality dimensions such as accuracy, completeness, consistency, and timeliness so it is fit for its intended business purpose.
Question 2: Which of the following is NOT typically considered one of the core dimensions of data quality?
- Accuracy
- Completeness
- Scalability (Correct answer)
- Timeliness
Correct answer: Scalability
Scalability is a system architecture concept, not a data quality dimension; the core dimensions include accuracy, completeness, consistency, timeliness, validity, and uniqueness.
Question 3: A CDO discovers that 15% of customer records have duplicate entries. Which data quality dimension is most directly affected?
- Accuracy
- Timeliness
- Uniqueness (Correct answer)
- Completeness
Correct answer: Uniqueness
Uniqueness measures whether each entity is recorded only once; duplicate records directly violate this dimension.
Question 4: What is a Data Quality Scorecard primarily used for?
- Tracking employee performance in data entry roles
- Measuring and communicating data quality metrics against defined thresholds (Correct answer)
- Monitoring network bandwidth used by data pipelines
- Auditing vendor contracts for data services
Correct answer: Measuring and communicating data quality metrics against defined thresholds
A Data Quality Scorecard provides a structured way to measure, visualize, and communicate data quality metrics relative to established targets and thresholds.
Question 5: Which approach involves correcting data quality issues at the point of data entry rather than after the fact?
- Retroactive cleansing
- ETL remediation
- Prevention-based data quality control (Correct answer)
- Post-processing validation
Correct answer: Prevention-based data quality control
Prevention-based data quality control embeds validation rules and constraints at the point of capture, stopping poor-quality data from entering systems in the first place.
Question 6: A CDO wants to implement a 'single source of truth' for customer data across the enterprise. Which practice best supports this goal?
- Data replication across multiple warehouses
- Master Data Management (MDM) (Correct answer)
- Data lake consolidation without governance
- Siloed departmental databases
Correct answer: Master Data Management (MDM)
Master Data Management (MDM) creates a single, authoritative, and consistent version of key business entities like customer data, serving as the single source of truth.
Question 7: Which metric measures the percentage of data records that pass all defined quality rules?
- Data Completeness Rate
- Data Accuracy Score
- Data Quality Pass Rate (Correct answer)
- Data Freshness Index
Correct answer: Data Quality Pass Rate
The Data Quality Pass Rate (also called conformity rate) measures the percentage of records that conform to all defined quality rules and standards.
